Published October 12, 2022
| Version 1.0
Dataset
Open
BLASTNet Simulation Dataset
Authors/Creators
- 1. Stanford University
- 2. Sandia National Labs
- 3. University of Melbourne
Description
Go to https://blastnet.github.io/ to access and download this reacting and non-reacting flow physics simulations. The Bearable Large Accessible Scientific Training Network-of-Datasets (BLASTNet) is composed of:
- Direct involvement from the scientific community.
- Public Machine Learning (ML) repositories such as Kaggle.
- Lossy compression techniques for managing >100 GB data.
- An easily-accessible webpage (https://blastnet.github.io/).
Notes
URL: https://blastnet.github.io/
Files
Files
(337 Bytes)
| Name | Size | Download all |
|---|---|---|
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md5:e464bd4e53d38a65ab51a8ef819957f8
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337 Bytes | Download |
Additional details
Related works
- Is cited by
- Journal article: 10.1016/j.jaecs.2022.100087 (DOI)
- Conference paper: 10.48550/ARXIV.2207.12546 (DOI)
- Is supplement to
- Journal article: 10.1016/j.jaecs.2022.100087 (DOI)
- Conference paper: 10.48550/ARXIV.2207.12546 (DOI)
References
- Wai Tong Chung, Ki Sung Jung, Jacqueline H. Chen, Matthias Ihme (2022), BLASTNet: A call for community-involved big data in combustion machine learning, Applications in Energy and Combustion Science 12 pp. 100087.
- Wai Tong Chung, Ki Sung Jung, Jacqueline H. Chen, Matthias Ihme (2022), The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning, arXiv 2207.12546